R Notebook

# read in age data
age <- read_csv(here::here("raw_data/age_data.csv")) %>% 
  clean_names()

# create day, month, year and date columns
age_by_date <- age %>% 
  mutate(
    year = str_extract(week_ending, "^\\d{4}"),
    monthday = str_extract(week_ending, "\\d{4}$"),
    month = str_extract(monthday, "^\\d{2}"),
    day = str_extract(monthday, "\\d{2}$"),
    date = ymd(str_c(year, month, day)), .after = 1
  )

age_date_clean <- age_by_date %>% 
  filter(!age_group == "All ages")
  

# create plot of admissions by age
ggplotly(age_date_clean %>%
  group_by(date, age_group) %>% 
  summarise(avg_admissions = mean(number_admissions)) %>% 
  ggplot() +
  aes(x = date, y = avg_admissions, colour = age_group) +
  geom_line() +
    labs(title = "Mean Weekly Admissions by Age Group",
         x = "Date",
         y = "Mean Admissions",
         colour = "") +
    theme_bw())

age_by_date %>% 
  filter(sex == "All") %>% 
  count()

sex_data_clean <- age_by_date %>% 
  filter(!sex == "All")

ggplotly(sex_data_clean %>%
  group_by(date, sex) %>% 
  summarise(avg_admissions = mean(number_admissions)) %>% 
  ggplot() +
  aes(x = date, y = avg_admissions, colour = sex) +
  geom_line() +
    labs(title = "Mean Weekly Admissions by Gender",
         x = "Date",
         y = "Mean Admissions",
         colour = "") +
    theme_bw())

`summarise()` has grouped output by 'date'. You can override using the `.groups` argument.

ggplotly(dep_date %>%
             group_by(date, simd_quintile) %>% 
             summarise(avg_admissions = mean(number_admissions)) %>% 
             ggplot() +
             aes(x = date, y = avg_admissions, colour = simd_quintile) +
             geom_line() +
             labs(title = "Average Admissions by SIMD",
                  x = "Date",
                  y = "Average Admissions",
                  colour = "SIMD Quintile"))

`summarise()` has grouped output by 'date'. You can override using the `.groups` argument.